The AI made 3D furniture assets his team shares, good on the first try
The AI via Blender MCP made add-on plugins and a Geometry Nodes asset-library workflow distributed on an internal network, useful chairs and tables on first try.
A random mix from the whole library. Shuffle for a new set.
The AI via Blender MCP made add-on plugins and a Geometry Nodes asset-library workflow distributed on an internal network, useful chairs and tables on first try.
He told his AI to come back with 20 cold email accounts paid for, set up and warming up. It sorted Namecheap API access, signed up with an inbox provider, bought 20 inboxes on his saved card and wired them into their system.
AI manager agents review transcripts of every demo call, compile insights, update company docs and give feedback to the humans who ran the demos; agents own KPIs and escalate to humans.
He needed a foreign language voiceover on a video presentation at work. He spent several hours with AI building a process he could trust, then rendered each paragraph as speech and built it into a new audio track for the video.
Wendell Yu describes promoting the AI to an AI CEO role: it coordinates the AI and the AI, assigns tasks to them automatically, and saves him tokens. It is the second of five first-hand the AI cases in his video.
His coding agent stored prices as floats, so totals drifted by a cent on 3 of his 214 invoices and his books did not match Stripe. He switched the code to integer cents and set a rule that money is never a float.
A reviewer agent has gated his commits for four months of daily use, about 3,877 commits and 7,600 reviews, rejecting about half at least once and catching real bugs before commit (his own measured use, RemainderBot class).
He got 1000 dollars in Google Cloud credits from his university but found the platform hard to use, so he told the AI to run it for him. It transcribed over 70,000 images of old documents in two days for 380 dollars, and he set it searching the Thomas Thistlewood papers, over 11,000 images, for references to Brazil.
Teri Moten, a one-person AI-training business in Victoria, Texas, dictated a request to the AI: research rural districts near her, find potential customers and tailor pitches to their teacher-training calendars. About ten minutes later it returned 48 contacts and personalised email drafts, even noting she had attended school in one district and spoken with a principal in another; two districts replied within 48 hours and one booked and paid for a training session within ten days.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.